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Introduction to OpenMP. Introduction OpenMP basics OpenMP directives, clauses, and library routines. Motivating example. Pthread is too tedious: explicit thread management is often unnecessary Consider the matrix multiply example - PowerPoint PPT Presentation
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Introduction to OpenMP
• Introduction
• OpenMP basics
• OpenMP directives, clauses, and library routines
Motivating example
• Pthread is too tedious: explicit thread management is often unnecessary– Consider the matrix multiply example
• We have a sequential code, we know which loop can be executed in parallel; the program conversion is quite mechanism: we should just say that the loop is to be executed in parallel and let the compiler do the rest.
• OpenMP does exactly that!!!
What is OpenMP?
• What does OpenMP stands for?– Open specifications for Multi Processing via collaborative work
between interested parties from the hardware and software industry, government and academia.
• OpenMP is an Application Program Interface (API) that may be used to explicitly direct multi-threaded, shared memory parallelism.
• API components: Compiler Directives, Runtime Library Routines. Environment Variables
• OpenMP is a directive-based method to invoke parallel computations on share-memory multiprocessors
What is OpenMP?
• OpenMP API is specified for C/C++ and Fortran.• OpenMP is not intrusive to the original serial
code: instructions appear in comment statements for fortran and pragmas for C/C++.
• OpenMP website: http://www.openmp.org– Materials in this lecture are taken from various
OpenMP tutorials in the website and other places.
Why OpenMP?
• OpenMP is portable: supported by HP, IBM, Intel, SGI, SUN, and others– It is the de facto standard for writing shared memory
programs.
– To become an ANSI standard?
• OpenMP can be implemented incrementally, one function or even one loop at a time.– A nice way to get a parallel program from a sequential
program.
How to compile and run OpenMP programs?
• Gcc 4.2 and above supports OpenMP 3.0– gcc –fopenmp a.c– Try example1.c
• To run: ‘a.out’– To change the number of threads:
• setenv OMP_NUM_THREADS 4 (tcsh) or export OMP_NUM_THREADS=4(bash)
OpenMP execution model
• OpenMP uses the fork-join model of parallel execution.– All OpenMP programs begin with a single master thread.
– The master thread executes sequentially until a parallel region is encountered, when it creates a team of parallel threads (FORK).
– When the team threads complete the parallel region, they synchronize and terminate, leaving only the master thread that executes sequentially (JOIN).
OpenMP general code structure
#include <omp.h> main () { int var1, var2, var3; Serial code . . . /* Beginning of parallel section. Fork a team of threads. Specify
variable scoping*/ #pragma omp parallel private(var1, var2) shared(var3) { /* Parallel section executed by all threads */ . . . /* All threads join master thread and disband*/ } Resume serial code . . . }
Data model
• Private and shared variables
•Variables in the global data space are accessed by all parallel threads (shared variables).
• Variables in a thread’s private space can only be accessed by the thread (private variables)
• several variations, depending on the initial values and whether the results are copied outside the region.
#pragma omp parallel for private( privIndx, privDbl )
for ( i = 0; i < arraySize; i++ ) { for ( privIndx = 0; privIndx < 16; privIndx++
) { privDbl = ( (double) privIndx ) / 16; y[i] = sin( exp( cos( - exp( sin(x[i]) ) ) ) ) +
cos( privDbl ); } } Parallel for loop index is
Private by default.
OpenMP directives
• Format:#progma omp directive-name [clause,..] newline
(use ‘\’ for multiple lines)
• Example:#pragma omp parallel default(shared) private(beta,pi)
• Scope of a directive is one block of statements { …}
Parallel region construct
• A block of code that will be executed by multiple threads.#pragma omp parallel [clause …] { ……} (implied barrier)
Example clauses: if (expression), private (list), shared (list), default (shared | none), reduction (operator: list), firstprivate(list), lastprivate(list)
– if (expression): only in parallel if expression evaluates to true– private(list): everything private and local (no relation with
variables outside the block).– shared(list): data accessed by all threads– default (none|shared)
• The reduction clause:
Sum = 0.0;#pragma parallel default(none) shared (n, x) private (I) reduction(+ : sum) { For(I=0; I<n; I++) sum = sum + x(I);}
– Updating sum must avoid racing condition– With the reduction clause, OpenMP generates code
such that the race condition is avoided.
• Firstprivate(list): variables are initialized with the value before entering the block
• Lastprivate(list): variables are updated going out of the block.
Work-sharing constructs
• #pragma omp for [clause …]• #pragma omp section [clause …]• #pragma omp single [clause …]
• The work is distributed over the threads• Must be enclosed in parallel region• No implied barrier on entry, implied barrier on
exit (unless specified otherwise)
• Schedule clause (decide how the iterations are executed in parallel):schedule (static | dynamic | guided [, chunk])
Synchronization: barrier
For(I=0; I<N; I++) a[I] = b[I] + c[I];
For(I=0; I<N; I++) d[I] = a[I] + b[I]
Both loops are in parallel regionWith no synchronization in between.What is the problem?
Fix: For(I=0; I<N; I++) a[I] = b[I] + c[I];
#pragma omp barrier
For(I=0; I<N; I++) d[I] = a[I] + b[I]
Critical session
For(I=0; I<N; I++) { …… sum += A[I]; ……}
Cannot be parallelized if sum is shared.
Fix:For(I=0; I<N; I++) { …… #pragma omp critical { sum += A[I]; } ……}
OpenMP runtime environment
• omp_get_num_threads
• omp_get_thread_num
• omp_in_parallel
• Routines related to locks
• ……
Sequential Matrix Multiply
For (I=0; I<n; I++)
for (j=0; j<n; j++)
c[I][j] = 0;
for (k=0; k<n; k++)
c[I][j] = c[I][j] + a[I][k] * b[k][j];
OpenMP Matrix Multiply
#pragma omp parallel for private(j, k)
For (I=0; I<n; I++)
for (j=0; j<n; j++)
c[I][j] = 0;
for (k=0; k<n; k++)
c[I][j] = c[I][j] + a[I][k] * b[k][j];
Travelling Salesman Problem(TSP)
• The map is represented as a graph with nodes representing cities and edges representing the distances between cities.
• A special node (cities) is the starting point of the tour.
• Travelling salesman problem is to find the circle (starting point) that covers all nodes with the smallest distance.
• This is a well known NP-complete problem.
Sequential TSP
Init_q(); init_best();
While ((p = dequeue()) != NULL) {
for each expansion by one city {
q = addcity (p);
if (complete(q)) {update_best(q);}
else enqueue(q);
}
}
OpenMP TSP
Do_work() { While ((p = dequeue()) != NULL) { for each expansion by one city { q = addcity (p); if (complete(q)) {update_best(q);} else enqueue(q); } }}
main() { init_q(); init_best(); #pragma omp parallel for for (i=0; I < NPROCS; i++) do_work();}
• Summary:– OpenMP provides a compact, yet powerful
programming model for shared memory programming• It is very easy to use OpenMP to create parallel programs.
– OpenMP preserves the sequential version of the program
– Developing an OpenMP program:• Start from a sequential program
• Identify the code segment that takes most of the time.
• Determine whether the important loops can be parallelized– The loops may have critical sections, reduction variables, etc
• Determine the shared and private variables.
• Add directives
OpenMP discussion
• Ease of use– OpenMP takes cares of the thread maintenance.
• Big improvement over pthread.
– Synchronization• Much higher constructs (critical section, barrier).• Big improvement over pthread.
• OpenMP is easy to use!!
OpenMP discussion
• Expressiveness– Data parallelism:
• MM and SOR• Fits nicely in the paradigm
– Task parallelism: • TSP• Somewhat awkward. Use OpenMP constructs to
create threads. OpenMP is not much different from pthread.
OpenMP discussion
• Exposing architecture features (performance):– Not much, similar to the pthread approach
• Assumption: dividing job into threads = improved performance.
• How valid is this assumption in reality?– Overheads, contentions, synchronizations, etc
– This is one weak point for OpenMP: the performance of an OpenMP program is somewhat hard to understand.
OpenMP final thoughts
• Main issues with OpenMP: performance– Is there any obvious way to solve this?
• Exposing more architecture features?
– Is the performance issue more related to the fundamantal way that we write parallel program?
• OpenMP programs begin with sequential programs.• May need to find a new way to write efficient
parallel programs in order to really solve the problem.